Welcome to the 14th installment of our blog series “My Path to Google.” These are real stories from Googlers, interns, and alumni highlighting how they got to Google, what their roles are like, and even some tips on how to prepare for interviews.

Today’s post is all about Julius Adebayo. Read on!

Can you tell us a bit about yourself?

I grew up in Nigeria, and came to the US for college. I studied mechanical engineering in undergrad, but started to drift towards machine learning (ML) around my last year. Afterwards, I ended up pursuing a master’s degree in computer science, focused on machine learning, and another in technology policy. In general, I am interested in research that tries to provide guarantees that deployment of machine learning in the real-world will be safe and reliable. My focus has been in studying bias, interpretability, and privacy/security all within an ML context. I also enjoy thinking about the intersection of machine learning and policy, especially how current advancements will affect daily life down the line. Outside of school and work, I enjoy listening to Jazz and Nigerian music in all its glory. I like playing soccer, and watching the NBA. Lately, I have become more interested in trying to spread machine learning knowledge to places in West Africa where machine learning expertise is not abundant.

What’s your role at Google?

I am one of the current residents in the Google AI Residency Program. The goal is to collaborate with researchers and engineers on the Google Brain team to do deep learning research. Deep learning research is new to me, and I am actually coming to it as a skeptic. There is a famous quote attributed to Von Neumann that says, "With four parameters I can fit an elephant. Give me five and I can make it wiggle its tail." The point of that quote is you typically want models that don't have too many parameters because you could make such models do anything. However, deep learning models tend to violate that requirement. Since being here, I have come to appreciate working with neural networks. There is a vibrant community here that is actively working to address several problems with the current models, especially in regards to security, potential bias, and stability of machine learning models. The work I am doing now is focused on assessing the performance of neural network explanation methods. (This link is closely related.)

What inspires you to come in every day?

The Google Brain team has several researchers and engineers who are working on really interesting projects. Talking to other residents and researchers, I find that I leave every conversation having learned something new. The breadth and depth of research on the team is incredible and it is quite fun to be in an environment like that.

Can you tell us about your decision to enter the process?

I found out about the residency program through a friend. As someone working at a startup doing ML, it was impossible to not hear about deep learning on a daily basis. I figured the residency would be a way to try and get to the cutting edge of work in this area as fast as possible. The Google Brain team has several researchers doing really interesting work. I remember reading some papers from ICLR, and noticed that a few of the papers I enjoyed reading came from researchers on the team.

How did the recruitment process go for you?

The recruitment process was quite smooth. I felt like I was aware of what was required at each stage, and I found the recruiters to be accommodating to my requests or questions. I was also given an opportunity to talk to a few researchers on the team.

What do you wish you’d known when you started the process?

Google can be overwhelming, especially given the concentration of expertise on the team. I would be more open to asking questions and reaching out to people doing interesting work.

Can you tell us about the resources you used to prepare for your interview or role?

The residency interview had a coding and research portion.

I had gone through software engineering interviews before, so my preparation there was using the whiteboard type experience I already had in that context. For the research angle, I went through a few deep learning papers that I found interesting, and tried to understand them. A few of the papers were discussed extensively in some of my interviews. I also spent some time reviewing past research I had done, so I could explain it well to others.